PANS：用于实时鲁棒支气管镜定位的概率气道导航系统
PANS: Probabilistic Airway Navigation System for Real-time Robust Bronchoscope Localization
AI总结：
针对现有视觉支气管镜定位方法鲁棒性不足、实时性差、泛化弱的问题，提出融合DMI与BSA模块的概率气道导航系统PANS，经临床数据集验证，在定位精度、鲁棒性、泛化性和实时效率上均优于现有方法，可提升肺部介入手术的安全性与质量。
摘要：
精准的支气管镜定位可为气道导航提供六自由度（6-DOF）信息，对肺部介入手术至关重要。然而，现有基于视觉的方法在临床实践中鲁棒性往往不足，难以实现实时运行，也难以泛化到训练阶段未见过的病例。为解决这些挑战，我们提出了一种新型概率气道导航系统（Probabilistic Airway Navigation System，PANS），该系统结合蒙特卡洛方法，通过位姿假设与似然估计实现鲁棒实时的支气管镜定位。具体而言，PANS 融合了多种视觉表征（例如里程计和地标），包含两个核心模块：基于深度的运动推断（Depth-based Motion Inference，DMI）和支气管语义分析（Bronchial Semantic Analysis，BSA）。为生成 PANS 所需的支气管镜位姿假设，我们设计了 DMI 模块，可随时间精准传播位姿假设的估计结果。此外，为估计准确的位姿似然，我们设计了 BSA 模块，可有效区分内窥镜图像中相似的支气管区域，同时提出了一种新的度量指标，用于评估估计深度图与分割得到的气道结构之间的一致性。在这种概率框架下，PANS 能够实现六自由度支气管镜定位，且具备更高的精度与鲁棒性。我们在采集的包含10个临床病例的肺部介入数据集上开展了大量实验，结果证实，在更深气道分支定位的鲁棒性与泛化性、以及实时推理效率方面，PANS 均优于现有最优方法。所提出的 PANS 有望成为手术室中的可靠工具，提升肺部介入手术的质量与安全性。
英文摘要：
Accurate bronchoscope localization is essential for pulmonary interventions, by providing six degrees of freedom (DOF) in airway navigation. However, the robustness of current vision-based methods is often compromised in clinical practice, and they struggle to perform in real-time and to generalize across cases unseen during training. To overcome these challenges, we propose a novel Probabilistic Airway Navigation System (PANS), leveraging Monte-Carlo method with pose hypotheses and likelihoods to achieve robust and real-time bronchoscope localization. Specifically, our PANS incorporates diverse visual representations (\textit{e.g.}, odometry and landmarks) by leveraging two key modules, including the Depth-based Motion Inference (DMI) and the Bronchial Semantic Analysis (BSA). To generate the pose hypotheses of bronchoscope for PANS, we devise the DMI to accurately propagate the estimation of pose hypotheses over time. Moreover, to estimate the accurate pose likelihood, we devise the BSA module by effectively distinguishing between similar bronchial regions in endoscopic images, along with a novel metric to assess the congruence between estimated depth maps and the segmented airway structure. Under this probabilistic formulation, our PANS is capable of achieving the 6-DOF bronchoscope localization with superior accuracy and robustness. Extensive experiments on the collected pulmonary intervention dataset comprising 10 clinical cases confirm the advantage of our PANS over state-of-the-arts, in terms of both robustness and generalization in localizing deeper airway branches and the efficiency of real-time inference. The proposed PANS reveals its potential to be a reliable tool in the operating room, promising to enhance the quality and safety of pulmonary interventions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
